Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer

The dynamic ground subsidence due to underground mining is a complicated time-dependent and rate- dependent process. Based. on the theory of rock rheology and probability integral method, this study developed the superposltlOn model for the prediction and analysis of the ground dynamic subsidence in mining area of thick !oose layer. The model consists of two parts （the prediction of overlying bedrock and the prediction of thick loose layer）. The overlying bedrock is regarded as visco-elastic beam, of which the dynamic subsidence is predicted by the Kelvin visco-elastic rheological model. The thick loose layer is regarded as random medium, and the ground dynamic subsidence, is predicted by the probability integral model. At last, the two prediction models are vertically stacked in the same coordinate system, and the bedrock dynamic subsidence is regarded as a variable mining thickness input into the prediction model of ground dynamic subsidence. The prediction results obtained were compared w^th actual movement and deformation data from Zhao I and Zhao II mine, central China. The agreement of the prediction results with the. field measurements.show that the superposition model （SM） is more satisfactory and the formulae obtained are more effective than the classical single probability Integral model（SPIM）, and thus can be effectively used for predicting the ground dynamic subsidence in mining area of thick loose layer.

Hydrodynamics Research on Amphibious Vehicle Systems:Modeling Theory

For completing the hydrodynamics software development and the engineering application research on the amphibious vehicle systems, hydrodynamic modeling theory of the amphibious vehicle systems is elaborated, which includes to build up the dynamic system model of amphibious vehicle motion on water, gun tracking-aiming-firing, bullet hit and armored check-target, gunner operating control, and the simulation computed model of time domain for random sea wave.

Statistical Approaches to Mapping QTL of Dynamic Traits

Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to treat phenotypic values collected at different time points as repeated measurements of the same trait, which are analyzed in the framework of multivariate theory. Alternatively, a growth curve may be fit to the phenotypes at multiple time points and inference can be made through the parameters of the growth trajectories. The latter has been used in QTL mapping for developmental traits and resulted in an appearance of the functional mapping strategy. Aiming at the disadvantages of functional mapping strategy, we propose to replace the nonlinear and non-additive model biological meaningful by the orthogonal polynomial or B-Spline model to fit dynamic curves with arbitrary shape and analyze arbitrary complicated data, and the constant residual covariance matrix by the alterable one calculated by using auto-correlation function to deal with discrepancies in measurement schedule of phenotype among progenies. A novel RRM mapping strategy was developed for mapping QTL of dynamic traits, which performs higher detecting efficiency than functional mapping, especially for detection of multiple QTL, has been proved by our simulations and data analysis. Finally, a simplified and effective mapping strategy was further discussed by integrating functional mapping and RRM mapping strategies.

Dynamic Resonant Frequency Bands of Suspension System of Vehicles with Varying Speeds Based on Time⁃Frequency Spectrum

The dynamic responses of suspension system of a vehicle travelling at varying speeds are generally nonstationary random processes,and the non-stationary random analysis has become an important and complex problem in vehicle ride dynamics in the past few years.This paper proposes a new concept,called dynamic frequency domain(DFD),based on the fact that the human body holds different sensitivities to vibrations at different frequencies,and applies this concept to the dynamic assessment on non-stationary vehicles.The study mainly includes two parts,the first is the input numerical calculation of the front and the rear wheels,and the second is the dynamical response analysis of suspension system subjected to non-stationary random excitations.Precise time integration method is used to obtain the vertical acceleration of suspension barycenter and the pitching angular acceleration,both root mean square(RMS)values of which are illustrated in different accelerating cases.The results show that RMS values of non-stationary random excitations are functions of time and increase as the speed increases at the same time.The DFD of vertical acceleration is finally analyzed using time-frequency analysis technique,and the conclusion is obviously that the DFD has a trend to the low frequency region,which would be significant reference for active suspension design under complex driving conditions.

Complex dynamics of a magnetic microrobot driven by single deformation soft tail in random environment

This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment.The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics.This paper establishes stochastic dynamic models for the microrobot before and after deformation,considering the influence of Gaussian white noise.The system responses are analyzed via steady-state probability density functions and first deformation time.The effects of different magnetic field strengths and fluid viscosities on the movement speed and angular velocity of the microrobot are studied.The results indicate that random disturbances can cause deformation of microrobots in advance compared to the deterministic case.This work contributes to the design and motion control of microrobots and enhances the theoretical foundation of microrobots.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

Objective:To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients.Methods:In this study,16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis.Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles,we recorded the eye movement signals and calculated eye movement features.Meanwhile,the stroke recognition models based on eye movement features were further trained by using random forest(RF),k-nearest neighbors(KNN),decision tree(DT),gradient boosting classifier(GBC),XGBoost,and CatBoost.Results:The stroke group and the healthy group showed significant differences in some eye movement features(P<.05).The models trained based on eye movement characteristics had good performances in recognizing stroke individuals,with accuracies ranging from 77.40%to 88.45%.Under the red stimulus,the eye movement model trained by RF became the best machine learning model with a recall of 84.65%,a precision of 86.48%,a F1 score of 85.47%.Among the six algorithms,RF and CatBoost performed better in classification.Conclusion:This study pioneers the application of traditional Chinese medicine's five-color stimuli to visual observation tasks.On the basis of the combined design,the eye-movement models can accurately identify stroke,and the developed high-performance models may be used in daily life scenarios.

车辆-轨道系统动力极值预测及可靠度计算

为从车辆-轨道系统的动力极值预测及其可靠度计算角度分析车辆系统行车安全、衡量系统可靠程度,基于车辆-轨道耦合动力学及概率理论,提出一种车轨系统动力极值预测及可靠度评估方法。首先,基于轨道不平顺概率模型,采用轨道不平顺累计概率谱确定具有不同激振能量的随机不平顺序列;依据离散Parseval定理,建立轨道不平顺时域序列和功率谱间的对应关系;将轨道不平顺概率模型与车辆-轨道耦合动力学模型相结合,从随机动力计算中提取车辆-轨道的动力极值及可靠度信息;随后利用车辆-轨道耦合动力模型及二维插值算子,根据谱线能量关系实现动力极值预测;此外,基于此系统激励输入与响应输出间的概率等效性,导出了动力极值的可靠度计算式。以实测的轨道不平顺和车体加速度数据及4种类型不平顺在不同累计概率下的谱组合计算得出的轮轨横向力和钢轨垂向位移的计算结果为样本,与预测结果进行对比。研究结果表明:计算时程的分布趋势基本与实测时程一致,动力极值预测结果与实际的动力计算结果较为接近。由于轨道不平顺随机性的存在使得车辆-轨道系统的动力响应存在随机离散及方差特征,但通过轨道不平顺的幅频及能量特性预测车辆-轨道系统的动力极值的方法是较为可行的。

外部随机激励干扰下掘进机振动信号多传感采集方法

掘进机运行过程中,在外部随机激励源的作用下,造成多传感器模式下的传感敏感性波动,结合振动信号不稳定,会导致采集的振动信号大幅度失真。为保证掘进机的稳定运行,提出多传感器模式下的随机激励振动信号采集方法。将掘进机看作多自由度系统,建立设备动力学模型;结合掘进机动力学特征,利用虚拟激励法构建符合外部源的随机激励函数;在建立的随机激励下,选择传感器类型和参数并安装多个传感器,设定采样频率;根据正弦稳态校准方法需求,设计校准传感器采集系统,提高传感器敏感性;在随机激励函数约束下预测传感器输出延时,计算每次测量的同步误差和误差补偿值,通过补偿确保多传感器实现振动信号的同步动态采集。实验结果表明:在多传感模式下,这种方法降低了外部的随机迟滞干扰,在随机激励作用下采集到的掘进机多传感振动信号波形与实际波形相符。

冻融循环下玄武岩纤维混凝土冲击力学性能预测模型

冻融循环下纤维混凝土的劣化规律是寒区服役混凝土工程安全性和耐久性评价的重要依据,现有服役混凝土工程的安全性和耐久性评价的研究具有工作量大、成本高、周期长等特点,构建基于机器学习的高精度力学性能预测模型已成为本领域研究热点。为探究冻融循环后玄武岩纤维混凝土冲击力学性能的高精度预测模型,采用SHPB装置对冻融循环后BFRC开展动态冲击压缩力学性能试验,并构建机器学习-Optuna混合预测模型,对60组以玄武岩纤维体积掺量、冻融循环次数、动荷载冲击速度为影响因素建立的动态峰值应力样本数据集进行预测。结果表明:k近邻、Lasso、多层感知机、极度梯度提升树和随机森林5种经典机器学习模型的预测准确度均较高,说明机器学习算法对于冻融循环后BFRC动态力学性能预测具有良好的预测效果,其中随机森林算法为最优预测算法;RF-Optuna混合预测模型显示出0.9754的拟合优度,具有良好的预测精度;非数据集工况预测表明,该混合模型对于各影响因素均具有良好泛化能力。研究成果可为冻融循环条件下BFRC动态力学性能的快捷精准预测提供参考。

Random Weighting Estimation Method for Dynamic Navigation Positioning

This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.

基于随机森林模型的造纸废水出水质量预测方法

为提升造纸企业废水出水质量预测的自动化水平,强化废水处理能力,研究采用增广矩阵对造纸废水原始数据实施升维处理,进而通过动态慢性特征分析方法确定最佳特征数量。在此基础上,通过随机森林模型对当前特征数量下的样本数据进行回归预测分析。为验证动态慢性特征-随机森林复合模型的有效性,统计分析了该模型的预测误差并与其他模型进行对比,发现动态慢性特征-随机森林复合模型预测结果的平均绝对百分比误差(MAPE)、均方根误差(RMSE)和决定系数R^(2)分别为0.02、2.88、0.81,显著优于传统的慢性特征-支持向量回归复合模型,显示出了较为理想的造纸废水出水质量预测准确度水平,具有一定的应用价值。

Pattern dynamics and bifurcation in delayed SIR network with diffusion network

The spread of infectious diseases often presents the emergent properties,which leads to more dificulties in prevention and treatment.In this paper,the SIR model with both delay and network is investigated to show the emergent properties of the infectious diseases'spread.The stability of the SIR model with a delay and two delay is analyzed to illustrate the effect of delay on the periodic outbreak of the epidemic.Then the stability conditions of Hopf bifurcation are derived by using central manifold to obtain the direction of bifurcation,which is vital for the generation of emergent behavior.Also,numerical simulation shows that the connection probability can affect the types of the spatio-temporal patterns,further induces the emergent properties.Finally,the emergent properties of COVID-19 are explained by the above results.

A novel imprecise stochastic process model for time-variant or dynamic uncertainty quantification

This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise probability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the crosscorrelation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliability analysis approach is developed based on the P-box-based imprecise stochastic process model,through which the interval of dynamic reliability for a structure under uncertain dynamic excitations or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples.

风浪作用下海上风电结构动力响应数值仿真

为了能够更好利用海洋资源,提高发电效率,提出了风浪作用下海上风电结构动力响应数值仿真方法。建立波动力平衡方程获取海波谱,计算出风场平均波高与周期。利用离散频谱逼近目标随机过程,用互功率频谱密度矩阵求拟脉动风场的风速时程曲线,将平均风速和谐波叠加风速相加,得到单位高度风荷载。按照拟层流风波产生机理,构建波浪谱模型,采用莫里森公式计算结构波浪荷载,考虑风浪间的相关性,将波浪参数代入风浪谱,得到风电结构响应数值。通过仿真得出在不考虑波面高度变动的情况下,波面力在波峰处被低估,在波谷处被高估,波面高度的变化会对风电结构动态响应产生一定影响,得出结果可以为保障电能效益提供参考。

生物氧化预处理过程pH值随机分布控制方法研究

生物氧化预处理过程中氧化槽pH值是影响细菌活性的关键因素之一,而pH值输出形态分布不符合高斯分布,使传统的均值和方差难以描述输出pH值分布,本文提出一种对矿浆输出pH的概率密度函数(PDF)统计信息控制方法。首先,采用B样条逼近矿浆输出pH值的PDF统计信息;其次,针对权值向量之间的关系,利用动态神经网络(DNN)建立控制输入和权值向量之间的非线性动态模型,基于建立pH的PDF统计信息权值模型,设计滑模变结构控制器,通过构造Lyapunov函数进行稳定性分析;最后,实现输出PDF统计信息对目标PDF统计信息的跟踪。仿真结果验证了所提方法的有效性,为生物氧化预处理过程提供了新方法。

基于参数随机分布特性的多质点重载列车安全制动模型研究

列车虚拟联挂技术能够大幅提升线路运输能力,是轨道交通新兴技术的研究热点,其采用动态追踪技术构建新的安全制动模型,但已有研究只考虑了紧急制动、最短追踪距离等边界问题,无法适应重载列车领域,也未解决重载列车线路复杂、空气制动离散性强等问题。针对上述问题,文章提出一种基于参数随机分布特性的多质点重载列车安全制动模型。文章首先基于列车虚拟联挂系统的拓扑结构进行列车多质点动力学分析,建立车钩缓冲器模型,参照CBTC(基于通信的列车控制系统)安全模型,设计出重载列车的安全制动模型;然后,对该模型进行仿真分析,从纵向动力学平稳性角度评估跟随车采用不同导向安全措施的技术指标,并分析典型参数在随机变化时安全制动距离的分布函数特性;最后,进行列车运动学仿真试验。验证结果表明,所提模型在提升列车运行的安全置信度约为1时,能够大大缩短列车间的追踪距离(1.4km以下),提升了列车的运输效率及安全性。

细观结构不均匀性对混凝土动弯拉强度的影响

本文在随机骨料模型的基础上,提出了既能反映混凝土骨料分布随机性又能反映混凝土细观各相材料力学特性随机性的随机骨料随机参数模型。利用该模型并考虑材料的细观力学的应变率效应,对湿筛混凝土和全级配混凝土梁进行了三分点冲击弯拉断裂数值模拟,分析了混凝土材料参数的离散性、混凝土级配以及初始静预载水平对其动弯拉强度的影响。研究表明:(1)随混凝土材料的弹性模量和抗拉强度的离散性增大,静弯拉强度和动弯拉强度降低,但动弯拉强度增强系数得到提高;(2)一定条件下全级配的混凝土梁的动弯拉强度增强系数高于湿筛混凝土梁的动弯拉强度增强系数;(3)为了更准确地确定动弯拉强度增强系数,应该考虑混凝土级配和初始预静载水平所产生的应变率效应对其的影响。

全光纤电流互感器的建模与仿真技术研究

在全光纤电流互感器物理模型的基础上,推导出了非线性动态模型,经过等效近似,建立了全光纤电流互感器的线性离散模型,并根据该模型对互感器的动态特性进行了仿真分析.同时,根据全光纤电流互感器的噪声特征建立了随机噪声模型,然后用不同的随机过程来模拟发生各种随机噪声.仿真结果表明:所建模型是合理的,模拟产生的随机噪声能够反映光纤电流互感器中噪声的统计特性.

非完整约束下的机器人运动规划算法

由非完整约束定义提出一种改进RRT(快速搜索随机树)算法,解决受动力学约束的移动机器人运动规划问题.该算法将移动机器人的非完整约束条件与RRT搜索算法相结合.针对RRT算法在全局状态空间均匀随机搜索导致算法无谓耗费代价大的缺陷,引入目标偏向思想,并选择计算复杂度低的距离参数提高求解速度.通过几类典型的非完整约束下的机器人运动规划仿真实验,证实了该算法的有效性和高效性.

基于实测响应空间的负荷动特性分类原理与方法

负荷特性的分类与综合是解决负荷建模中时变性难题的有效途径。于是基于随机过程相关性原理,提出了一种基于实测响应空间的负荷动特性分类方法。该方法以负荷动特性的实测响应空间为特征向量空间,对各个样本的实测响应进行时间坐标的标准化,以计算实测响应空间各样本间的相关系数,并采用系统聚类法按相关系数值进行特性分类。实测响应空间具有能够直接、准确地反映负荷构成的本质特征的优点,一工程应用实例证明了该方法正确有效,实现简便。